Improving Open Government Data Performance of Local Governments: A Configurational Analysis Based on TOE Framework

Abstract

Open Government Data (OGD) is leading the way towards digitization, intelligence, and transparency of public services and government decision-making. Aiming at elucidating the configurational path of OGD performance of local governments, this paper employs the technology-organization-environment (TOE) framework and the fuzzy set qualitative comparative analysis (fsQCA) to explore the key factors and configuration paths of OGD performance from the cases of 31 provincial OGD practice in China. Results indicate that OGD performance depends on the combination of technical, organizational, and external environment conditions, and that there exist substitution relationships among the various preconditions for the improvement of OGD performance, including information infrastructure, technology application capability, data resources, economic strength, policy regulations, and inter-government competition, as discussed in previous TOE based research. Specifically, seven configuration paths are identified to achieve high-level OGD performance, namely, Technology-Organization-Environment combined driven, Economy-Talent-Demand driven, Institution-Data-Policies driven, Institution-Economy-Demand driven, Organization-Policies-Competition driven, Data-Economy-Demand driven, and Data-Policies-Competition driven. This research is of particular significance to achieve high-level OGD performance for local governments with different resources and environments.

Keywords

Open government data OGD performance TOE framework fsQCA configurational paths

1. Introduction

OGD refers to the behavior of government departments providing government data to citizens, enterprises, and other NGOs through OGD platforms. These data are primary, machine-processable, suitable for social utilization, and ensure that any stakeholders access, use, and reuse without restriction. Through the three main aims of OGD – transparency, releasing social and commercial values, and participation and engagement (Open Knowledge Foundation, 2012), OGD can provide us with political, economic, and social benefits (Zhang et al., 2005; OECD, 2010; Janssen et al., 2012; Jetzekt, 2013; McKinsey Global Institute, 2013; Attard et al., 2016). As the representative manifestation of the open government movement, OGD is leading the way towards digitization, intelligence, and transparency of public services and government decision-making.

Due to the substantial benefits, OGD has been implemented global wide, across various political, economic, and social domains, and reaching national, sub-national and municipal levels. In China, since Shanghai and Beijing became the trailblazers of OGD in 2012, it has been moving forward rapidly across provinces and most cities in China. In recent years, China has implemented strategic policies aimed at promoting OGD, which has encouraged local governments to independently explore OGD solutions, and it turned out that OGD performance across provincial areas are uneven. While economically developed Zhejiang Province and Shanghai have served as exemplary models for OGD, Guizhou Province, despite its economic underdevelopment, has achieved remarkable performance in OGD development. Shandong Province, which established its provincial OGD platform in 2018, has consistently ranked in the top 5 of the China Open Data Index from 2018 to 2022. Nevertheless, many provincial areas exhibit low levels of OGD performance. It is worthwhile to examine why some local governments excel in their OGD performance while others do not, despite similar policy incentives and resources. Specifically, understanding how regions with limited conditions can achieve high-levels OGD performance is intriguing. In this situation, to identify the factors affecting local OGD performance, and the relationships and/or combinations of those factors, if any, is of practical significance.

Causality plays a pivotal role in the analysis of stakeholder behavior. Examining causality in stakeholder behavior analysis is instrumental in constructing decision models and designing intelligent systems (Sim & Kim, 2018). Numerous empirical studies have been conducted on the factors impacting OGD performance, utilizing methodologies such as multidimensional variable correlation analysis, event history analysis, and cross-case comparisons. Previous research has utilized the technology-organization-environment (TOE) framework to investigate the influence of technology, organization, and external environment on OGD performance. Recent studies have also made valuable contributions by examining the key factors and mechanisms that explain OGD performance specifically for provincial and municipal governments (Zhao & Fan, 2021; Zhao et al., 2022). However, these studies have not yet provided distinct solutions for local governments to enhance their OGD performance, considering their diverse resource. This is particularly important due to variations in natural resource, policy systems, and industrial structures, which result in significant differences in information infrastructure, data resources, human resources, and economic development among provincial governments in China. Due to these differences, more researches need to incorporate additional technological, organizational, and environmental factors to differentiate itself from previous studies. In addition to factors such as technology application capability, financial investment, organizational development, policy and regulation, public demands, and inter-governmental competition (Wang & Lo, 2016; Zhao & Fan, 2021; Zhao et al., 2022), more empirical studies are still needed to consider the impact of information infrastructure, data resources, intellectual guarantee, and economic strength on the implementation and performance of OGD. Furthermore, existing research on provincial-level governments has focused on a limited number of cases with similar technological, organizational, and external environmental, and the generalizability of research findings to a broader range of cases remains to be validated. In other words, the practical guidance provided by existing literature regarding why some local governments excel in achieving high OGD performance while others have low OGD performance still has limitations. Therefore, there is a need for more provincial-level research from this perspective.

This research aims to investigate the technological, organizational, and environmental factors that are related to OGD performance from the perspective of configuration. The research questions are: (1) Which factors, such as technological, organizational, and external environmental factors, have an impact on the OGD performance of local governments? (2) What are the configuration paths, if any, of the factors influencing OGD performance? (3) Given the diversified technological conditions, organizational resources, and external environmental of local governments, what strategies should they adopted to achieve high levels of OGD performance? To address these research questions, this research constructed an analytical framework based on the TOE framework. Then we employ the fuzzy set qualitative comparative analysis (fsQCA) from the configurational perspective to explore the key factors and configuration paths of the differences in the level of local governments’ OGD performance in China.

The rest of this paper is structured as follows: Section 2 systematically reviews the related research on OGD development, OGD critical factors, and TOE framework. Section 3 introduces the analytical framework. Section 4 delineates the methodology and data, including a description of the measurement indicators and data collection procedure. Section 5 analyzes the results. Section 6 discuss the findings, followed by the conclusions in Section 7. Finally, the primary contributions, limitations and future studies are summarized in Section 8 and Section 9, respectively.

2. Literature review

2.1. OGD development and its performance evaluation

OGD is becoming a prominent focus on the agendas of government agencies across various levels in numerous countries. Since its founding in 2011, the global Open Government Partnership has grown to 75 countries and 104 local jurisdictions that work alongside thousands of civil society organizations (Open Government Partnership, 2023). Following the establishment of the pioneering OGD platform by the United States in 2009, other countries such as the United Kingdom, Australia, and Japan have also implemented their own OGD platforms. The UN e-Government 2020 report shows that the number of countries adopting the OGD portal has increased to 153 (UNDESA, 2020). In China, Shanghai took the lead in establishing the first OGD platform in 2012, leading to a rapid growth of the number of OGD platform since 2017. At the end of 2022, 208 provincial and urban local governments in China have launched their OGD platforms (DMG Lab, 2022).

Existing studies commence from different perspectives, such as government data resources, open platforms, and users, to build evaluation frameworks for evaluating OGD performance. Evaluations taking note of government data resources emphasize the features, quality, and availability of open datasets. The Open Data Monitor evaluates the degree of open data from the five aspects: number of datasets, open licenses, machine-readable, availability, and metadata completeness (Open Data Monitor, 2022). The Global Open Data Index emphasizes the quality and standards of data, including benchmarks such as data availability, free access, machine-readable, open licenses, raw data, and data quality (Global Open Data Index, 2017). Wang and Shepherd analyzed the OGD performance of the UK from these aspects – primary, timely, accessible, machine processable, non-discriminatory, non-proprietary, and license-free (Wang & Shepherd, 2020). For those evaluations centering on open data platforms, the emphasis is set on the functions, features, and data management capability of OGD platform. UN e-Government Survey evaluates the OGD performance of member countries in terms of the construction of government data platforms, open data content areas, and data access methods (UNDESA, 2020). The user-oriented evaluation focuses on estimating how the open data are used, like the PSI Scoreboard of Europe notes the state of release and reuse of information by public bodies (Susha et al., 2015). Evaluations from the system perspective consider broad factors that influence the pattern and performance of OGD. Granickas (2013) employed economic, political, and social indicators to measure the expected benefits of OGD on government, enterprises, and citizens. The Open Data Barometer measures how governments publish and use open data from the aspects of readiness for open data initiatives, implementation of open data programs, and the impact that open data has on business, politics, and civil society (World Wide Web Foundation, 2018). The Lab for Digital & Mobile Governance constructs an evaluation system from dimensions of readiness, platform, data set, and utilization, to present the OGD performance of local government (DMG Lab, 2022).

2.2. The influencing factors of OGD performance

Existing research has discussed some technological, organizational, and environmental factors, which have impact on OGD performance, as shown in Table 1. On the technical dimension, factors such as insufficient metadata are created and provided (Schuurman et al., 2008; Xiong et al., 2011), or the metadata is usually unstructured and difficult to understand (Dawes, 2010). Limiting the possibilities of data publish and usage (Zuiderwijk et al., 2012a) lead to difficulties in determining the quality of open data (Meijer et al., 2013). The fragmentation of open data (Kaasenbrood, 2013) and poor search function in the OGD platform (Zuiderwijk et al., 2012b) resulted in the need to expend lots of time and effort in using the open datasets. In addition, the lack of easy-to-use monitoring tools for user demands and behaviors, as well as the absence of a discussion environment would result in low motivation for user participation, and data providers cannot be aware of user needs and feedback (Janssen et al., 2012; Zuiderwijk et al., 2013a, 2013b; Zuiderwijk & Janssen, 2014a).

Table 1.
Factors impacting the performance of OGD.

Dimension Factors References

Technical factors Metadata Schuurman et al. (2008), Xiong et al. (2011), Dawes (2010), Zuiderwijk & Janssen (2014a)

Quality of the open data Meijer et al. (2013), Kaasenbrood (2013), Zuiderwijk & Janssen (2014a), Hossain et al. (2021)

Search function of OGD platform Zuiderwijk et al. (2012b), Zuiderwijk & Janssen (2014a)

Monitoring tools of user demands and discussion environment Janssen et al. (2012), Zuiderwijk et al. (2013a), Zuiderwijk et al. (2013b), Zuiderwijk & Janssen (2014a)

Technical management capability and skills Zhao & Fan (2021)

Technological infrastructure Hossain et al. (2021)

Perceived usefulness, benefits, risks, and barriers Yang & Wu (2016), Wang and Lo (2016)

Organizational factors Institutional arrangements and capability Wang & Lo (2016), Yang & Wu (2016), Safarov (2019), Zhao & Fan (2021)

Organizational culture Zhao & Fan (2021)

Operational professionals Hossain et al. (2021)

Public information and data resources Helbig et al. (2012), Moon (2020)

Financial resources Zhao & Fan (2021)

Environmental factors Policy, legal framework, and guidelines Zuiderwijk & Janssen (2014a), Susha et al. (2015), Safarov (2019), Moon (2020), Hossain et al. (2021), Zhao et al. (2022)

Risks avoidance culture Janssen et al. (2012)

Infringing privacy and legal Responsibilities of open data abuse Kalidien et al. (2010), Zuiderwijk & Janssen (2014a),

External pressures, or Intergovernmental competition Wang & Lo (2016), Hossain et al. (2021), Zhao & Fan (2021)

Public demands, support, and awareness Safarov (2019), Zhao & Fan (2021)

the role of citizens Moon (2020)

Educational support Safarov (2019)

Administrative value Moon (2020)

Interactions and relations among stakeholders Helbig et al. (2012)

Dimension	Factors	References
Technical factors	Metadata	Schuurman et al. (2008), Xiong et al. (2011), Dawes (2010), Zuiderwijk & Janssen (2014a)
	Quality of the open data	Meijer et al. (2013), Kaasenbrood (2013), Zuiderwijk & Janssen (2014a), Hossain et al. (2021)
	Search function of OGD platform	Zuiderwijk et al. (2012b), Zuiderwijk & Janssen (2014a)
	Monitoring tools of user demands and discussion environment	Janssen et al. (2012), Zuiderwijk et al. (2013a), Zuiderwijk et al. (2013b), Zuiderwijk & Janssen (2014a)
	Technical management capability and skills	Zhao & Fan (2021)
	Technological infrastructure	Hossain et al. (2021)
	Perceived usefulness, benefits, risks, and barriers	Yang & Wu (2016), Wang and Lo (2016)
Organizational factors	Institutional arrangements and capability	Wang & Lo (2016), Yang & Wu (2016), Safarov (2019), Zhao & Fan (2021)
	Organizational culture	Zhao & Fan (2021)
	Operational professionals	Hossain et al. (2021)
	Public information and data resources	Helbig et al. (2012), Moon (2020)
	Financial resources	Zhao & Fan (2021)
Environmental factors	Policy, legal framework, and guidelines	Zuiderwijk & Janssen (2014a), Susha et al. (2015), Safarov (2019), Moon (2020), Hossain et al. (2021), Zhao et al. (2022)
	Risks avoidance culture	Janssen et al. (2012)
	Infringing privacy and legal Responsibilities of open data abuse	Kalidien et al. (2010), Zuiderwijk & Janssen (2014a),
	External pressures, or Intergovernmental competition	Wang & Lo (2016), Hossain et al. (2021), Zhao & Fan (2021)
	Public demands, support, and awareness	Safarov (2019), Zhao & Fan (2021)
	the role of citizens	Moon (2020)
	Educational support	Safarov (2019)
	Administrative value	Moon (2020)
	Interactions and relations among stakeholders	Helbig et al. (2012)

Organizational and social environmental factors also have important influences on OGD performance. Policies and laws provide rules and guidance for OGD, which are considered the prerequisites for implementing OGD (Susha et al., 2015; Zhao et al., 2022). However, institutional barriers like the absence of a unified open data policy and culture of risk avoidance led to resistance to reforms (Janssen et al., 2012). Infringing privacy and legal responsibilities for abuse of open data are also important obstacles (Kalidien et al., 2010). Helbig et al. (2012) emphasized the publishing and usage of OGD limited by the capability of the government and users, internal government practice, as well as interactions and relations among government, data resources, data users, citizens, and other stakeholders. Safarov (2019) provides a comprehensive theoretical framework to indicate policies and strategies, organizational arrangements, related skills and educational support, public support, and awareness play important roles in OGD. Moon (2020) found that policies, public information and data, administrative value, and the role of citizens are the important factors of open government and the OGD movement. Some empirical studies have also found other influencing factors, which include perceived usefulness, perceived effort, perceived benefits, perceived barriers, perceived risk, technological infrastructure, technical management capability, management leadership, skilled operational professionals, organizational capability, organizational culture, financial resources, public demands, intergovernmental competition, and external pressures (Yang & Wu, 2016; Wang & Lo, 2016; Zhao & Fan, 2021; Hossain et al., 2021).

2.3. Technology-organization-environment (TOE) framework

The TOE framework, developed by Tornatzky and Fleisher (1990), is a comprehensive analytical framework that considers the contextual application of technology. It is widely adopted because of its ability to analyze the impact of multilevel factors, including technology, organizational structure, and external environment. Technological factors indicate the various technical means in the realm of information systems, the compatibility between the technology and the organization as well as whether the technology would bring forth potential benefits to the organization. Organizational factors only refer to the internal environment of an organization, consisting of organization structure, organization scale, degree of centralization and regularity, the complexity of the management structure, staff skills, and related resources. Environmental factors emphasize the external environment, covering the industry, competition, pressure from demands, systems, and so on (Baker, 2012).

The TOE framework boasts flexible content and an extensive range of application areas. Scholars have previously applied this framework to open systems (Chau & Tam, 1997), electronic data interchange (Kuan & Chau, 2001), corporate e-commerce (Zhu et al., 2006), information and communication technology (Srivastava & Teo, 2010), enterprise resource planning (Zhu et al., 2010), cloud computing (Lian et al., 2014), e-government (Thi et al., 2014), financial transparency (Chen et al., 2019), and big data analytics (El-Haddadeh et al., 2021). The TOE framework also has wide-ranging applications in elucidating the underlying mechanisms that impact OGD performance. A study conducted by Yavuz and Welch (2014) delved into the managemental, organizational, and environmental factors that influence transparency and interactivity features on local government websites. Tsou and Hsu (2015) proposed three key background factors that facilitate openness: technological background, organizational background, and environmental background. The TOE framework was utilized by Wang and Lo (2016) to explore factors that led to the adoption of OGD. Similarly, Zhao and Fan (2021) researched the impact of technology, organization, and environmental dimensions on OGD performance by employing the TOE framework and resource-based theory. Furthermore, Hossain et al. (2021) put forth a model to probe into the influential factors of OGD performance based on the TOE framework.

3. Analytical framework

This research argues that technology, organization, and environment are key dimensions that affect OGD performance. In the process of OGD, stakeholders of various types, with different responsibilities or interest demands, collaborate and interact with each other (Heimstädt et al., 2014; Ohemeng & Ofosu-Adarkwa, 2015; Wijnhoven et al., 2015; Dawes et al., 2016; Styrin et al., 2017), supported by OGD policies and strategies (Shin & Choi, 2015; Dawes et al., 2016; Styrin et al., 2017), OGD management practices (Zuiderwijk et al., 2014b), technological software and infrastructure, as well as social and cultural resources (Shin & Choi, 2015; Styrin et al., 2017). That is, OGD involves dynamic interactions of technology conditions, organizational resources, and the external environment (Reggi et al., 2022), and all elements of OGD are interconnected. When one element changes, the entire system may be affected (Mercado-Lara & Gil-Garcia, 2014). OGD performance is not only affected by the intrinsic capability of government bodies and economic strength but also is constrained by the current level of technological development. The social context of OGD also affects its performance. Furthermore, these factors influence each other and act together on OGD performance.

The existence of “multicausal events” challenges the notion that all events are caused by a single factor. This raises important questions about human cognition, specifically regarding the construction and interpretation of these multicausal structures (Warfield, 2008). Unlike the majority of information system theories and models, the TOE framework reflects the impacts of multiple aspects (i.e., internal and external) on adoption decisions based on the following three contextual groups: technology, organization, and environment. Meanwhile, although previous studies have taken different factors to measure technological, organizational, and environmental influences, the core theme of the TOE framework is still that adoption of innovation is the outcome of combined influences of technology, organization, and environment (Chen et al., 2019). The TOE framework demonstrates a strong empirical support and is founded upon a solid theoretical basis. This robust foundation makes it highly flexible and adaptable, allowing for widespread application. Specifically, the TOE framework has proven to be an effective and feasible tool in comprehending the OGD performance. Previous researches have applied the TOE framework to study the impact factors of OGD implementation and the impact mechanisms of OGD performance (Wang & Lo, 2016; Zhao & Fan, 2021; Hossain et al., 2021). In this paper, the TOE framework serves as the fundamental basis that elucidates the impact factors of OGD performance, as shown in Fig. 1.

Figure 1.

Analytical framework.

In the technological dimension, the information infrastructure and technological application capability has priority for OGD implementation. In the organizational dimension, an organization that gathers senior management and a certain size of staff is necessary, and dominance of data resources and economic and financial strength also contribute to carrying out OGD work. Finally, user demands, policy regulations, and competitive pressures are indispensable drivers for OGD. Table 2 provides the descriptions of these specific variables in each dimension.

Table 2.

Variables selected of each dimension based on TOE frameworks.

Dimensions	Variables	References
Technical conditions	Information infrastructure	Bertot et al. (2010), Eckartz et al. (2014), Yavuz & Welch (2014), Zuiderwijk et al. (2015), Wang & Lo (2016), Gupta & George (2016)
	Technology application capability	Bertot et al. (2010), Fan (2013), Yavuz & Welch (2014), Lopez-Herrejon et al. (2015), Wang & Lo (2016), Zeleti & Ojo (2016), Gupta & George (2016), Grimmelikhuijsen & Feeney (2017)
Organizational resources	Institutional construction	World Bank (2015), Zhang & Chen (2015), Zhao & Fan, (2018)
	Data resources	Dawes et al. (2016), Carrasco & Sobrepere (2015), Huang (2019), Zhao & Fan (2018)
	Intellectual guarantee	Zhang & Chen (2015), Zhao & Fan, (2018)
	Economic strength	Attard et al. (2015), Yang et al. (2015), Zhao & Fan (2018)
	Financial input	Attard et al. (2015), Yang et al. (2015), Zhao & Fan (2018)
External environment	Policy regulations	Janssen et al. (2012), Zuiderwijk & Janssen (2014c), Dwivedi et al. (2015), Yang et al. (2015), Zuiderwijk et al. (2015b), Yang & Wu (2016), Safarov et al. (2017), Lnenicka & Komarkova (2019), Safarov (2020), Zhao et al. (2022)
	User demand	Barry & Bannister (2014), Zuiderwijk & Janssen (2014c), Yang & Wu (2016), Yang et al. (2015), Yang et al. (2016), Fan & Zhao (2017)
	Inter-government competition	Zuiderwijk & Janssen (2014c), Yang et al. (2015), Yang & Wu (2016), Zhao & Fan (2021), Zhao et al. (2022)

3.1. Technical conditions

Technology is the practical tool for the government to achieve its goals. Such technical conditions are measured by technological infrastructure, technical skills, and technological management capability (Wang & Lo, 2016; Gupta & George, 2016; Yavuz & Welch, 2014). The prominent characteristic distinguishing OGD from traditional open government information is that OGD has a high reliance on digital technologies. Therefore, in the current landscape, the significance of information technology in open government cannot be overemphasized (Gil-Garcia et al., 2020). Technical know-how and capacity constitute the primary determinants of the further advancement of OGD (Grimmelikhuijsen & Feeney, 2017). The integration of technology can offer an understanding of the utilization of open data and shed light on the various challenges related to providing and utilizing open data (Lee & Kwak, 2012; Linders, 2012; Sieber & Johnson, 2015; Wang & Lo, 2016; Ruijer & Meijer, 2020). The government’s capability to manage technological features, organizational structure, and internal relationships is a crucial factor that constrains the performance of the government’s informatization construction (Fan, 2013). Organizations with well-developed technology management capabilities and well-equipped support systems have a strong intention to open data (Bertot et al., 2010) and to build quality websites with better transparency and interactivity features to meet organizational needs (Berry & Berry, 1999; Dawes et al., 1999; Christensen & Hughes, 2000; Brown, 2001). Government departments should also provide application programming interfaces (API) for timely and integrated open data accessibility (Eckartz et al., 2014; Zuiderwijk et al., 2015). To better open and access data and create value, stakeholders must also have emerging technical skills (Zeleti & Ojo, 2016; Lopez-Herrejon et al., 2015). Therefore, this research has a focus on the impact of information infrastructure and technology management capabilities on OGD performance.

3.2. Organizational resources

The organization dimension is a kind of organizational resource affecting administrative decisions and ultimately impacts organizational performance (Mark et al., 2004; Lee & Whitford, 2013). Organizational capability is of particular importance to organizational performance (Wang & Zeng, 2017). OGD is a challenging endeavor that has the potential to disrupt established organizational boundaries, intensify workload, and potentially overburden the organization. This necessitates the establishment of a specialized OGD team, as well as unwavering senior leadership commitment to overcoming resistance and inertia, inspiring individuals to make necessary modifications promptly and effectually, and allocating explicit roles to personnel (World Bank, 2015; Zhang & Chen, 2015; Zhao & Fan, 2018). Government data is a foundational element of the OGD ecosystem (Dawes et al., 2016), and the government is the largest data holder (Carrasco & Sobrepere, 2015). In the era of big data, the integration of information from diverse sources and the dynamic composition and publication of data in various formats have become routine tasks (Huang, 2019). Governments should publish their data in machine-readable formats while updating and maintaining it regularly (Zhao & Fan, 2018). The implementation of OGD also requires a dedicated budget, and insufficient financial supply will inevitably cause a reduction in OGD incentives (Attard et al., 2015; Yang et al., 2015; Zhao & Fan, 2018). Therefore, factors such as the level of economic development and financial investment should also be included in the factors affecting government OGD. From aspects such as institutional construction, data resources, intellectual guarantee, level of economic development, and financial resources, this research explores the influence of organization resources on OGD performance.

3.3. External environment

Systems theory perceives that regulatory elements, norm elements, and cultural-cognitive elements significantly affect organizations through mandatory homogeneity, normative pressures, and the process of imitation, resulting in organization behaviors fitting the systems environment (Dimaggio & Powell, 1981; Scott, 1995). That is, norms, coercive pressures, and other similarly organized social influences can affect government behavior and performance (Bearfield & Bowman, 2017; Ingrams, 2017). In OGD practice, the government is under external pressures, such as mandatory demands from policies and rules, competition from other governments of the same level, and public demands (Zuiderwijk & Janssen, 2014c; Yang & Wu, 2016). The utility of system factors like policies and strategies, legislation, education, and public support enables a country to successfully implement OGD (Safarov, 2020). This means that higher levels of external influence to demand and promote OGD may increase an agency’s intention to release OGD (Yang & Wu, 2016). The pressure from public demand and advocacy for OGD has become an important driver for OGD (Yang et al., 2015; Fan & Zhao, 2017). Mass media and potential open data user groups such as businesses, nonprofits, and individuals play a vital role in driving institutional participation in open data initiatives by advocating for the open data movement through forums and social networking sites. They demand that governments share data with the public (Barry & Bannister, 2014; Yang et al., 2016; Fan & Zhao, 2017). While inadequate regulations remain the most significant barrier to OGD implementation (Janssen et al., 2012; Safarov et al., 2017; Yang et al., 2015), a sound OGD-related policy regime can offer guidance, rules, and operational provisions for open data practices (Lnenicka & Komarkova, 2019; Zhao et al., 2022). Policy strategies and authorization demands from the higher levels of government or national become momentum to encourage or thrust government institutions to implement open data (Zuiderwijk et al., 2015b). Healthy legal and regulatory systems also help to avoid issues of risks in data security and individual privacy (Dwivedi et al., 2015). Moreover, from the perspective of intergovernmental relations, competition among peer governments represents a critical external environmental condition that impacts government behavior and organizational performance (Bearfield & Bowman, 2017; Ma, 2014). The same-level or adjacent government competition significantly affects the implementation and performance of OGD initiatives (Zhao & Fan, 2021; Zhao et al., 2022), as heightened participation in open data practices by certain institutions may prompt others to take similar actions (Yang et al., 2015). This research mainly concerns the influences on OGD performance from the three external environmental factors: user demands, policy regulations, and inter-government competition.

4. Methods and data

4.1. Research methodology

QCA is a non-symmetric data analysis technology, that employs qualitative and quantitative evaluations to calculate the extent of affiliation in the cases, thereby discerning different configurations constituting sufficient and necessary conditions of given results (Ragin, 1987, 2008; Schneider & Wagemann, 2012). Based on holistic thinking, QCA considers cases consisting of causal variables as a whole. This enables QCA to be applicable for studies with very small (<50) to very large (thousands) numbers of samples (Ragin, 1987; Aversa et al., 2015). QCA also utilizes calibrated measures to transform data into a range of 0 to 1. This enables qualitative researchers to interpret both relevant and irrelevant variation, while also allowing quantitative researchers to accurately position cases about each other. There are three types of QCA –crisp set QCA (csQCA), multi-value QCA (mvQCA), and fuzzy-set QCA (fsQCA). Since csQCA considers variables to be binary, it therefore cannot fully capture the complexity of the level or extent instigated by naturally evolving situations (Rihoux & Ragin, 2009). For mvQCA, there are also discussions about its usability and pitfalls (Thiem, 2013; Maarten et al., 2009; Vink & Vliet, 2013). Through fuzzy sets and combining the principle of fuzzy logic with the QCA principle, fsQCA extended csQCA, enabling itself to be widely applied to information systems (Pappas & Woodside, 2021), online consumption and marketing (Pappas, 2018), strategic and organizational studies (Greckhamer et al., 2018), education and learning performance (Papamitsiou et al., 2018), big data analysis (Vatrapu et al., 2016) and policy promotion (Zhang & Guo, 2020).

Since the OGD promotion paths are not the same across regions, OGD implementation models in different regions find themselves under configurations of technological, organizational, and environmental dimensions (Safarov, 2019). FsQCA would help to ascertain factors influencing OGD performance and combinations of conditions could achieve high-level OGD performance (Kassen, 2018; Chen & Chang, 2020; Hossain et al., 2021; Zhao & Fan, 2021; Zhao et al., 2022). This paper employs fsQCA to investigate the impact of technological conditions, organizational resources, and the external environment on OGD performance.

4.2. Research sample

Due to the benefits of OGD, it is not uncommon for regional and municipal authorities to develop their own OGD platforms at the sub-national and local levels, respectively, in addition to actively promoting national OGD projects (Kassen, 2018). In China, OGD projects tend to be spearheaded by local governments, with their numbers growing rapidly. In this context, empirical research on OGD performance at the provincial level is particularly relevant. Accordingly, this research focuses on identifying key factors and configuration paths of local governments’ OGD performance, leveraging data from 31 provincial administrative regions across the mainland of China. Previous studies have already analyzed the OGD performance influencing factors at the local government level (Fan & Zhao, 2017; Wang & Lo, 2016; Zhao & Fan, 2021; Zhao et al., 2022), underscoring the importance of focusing on these issues. The China Local Government Open Data Report 2021 (DGM Lab, 2021) evaluated the OGD performance across the 31 provinces of China, providing critical insights and data sources for understanding the OGD performance of local governments.

4.3. Data collection

The outcome variable of this research is OGD performance, with the conditional variables including information infrastructure, technology application capability, institutional construction, intellectual guarantee, data resources, economic strength, financial input, public demand, policy regulations, and inter-government competition. The measurement indicators and data sources of variables are introduced in Table 3, the supplementary description of data sources is summarized in Appendix A, which is at the end of this study. The OGD performance indicator uses data from 2021, and those factors that affect OGD performance use data from 2020. It is important to acknowledge that the data associated with each indicator are sourced from published reports and can be acquired by Internet searching.

Table 3.
Measurement indicators and data sources of variables.

Variables Measurement indicators Data sources

OGD performance China Open Data Index China Local Government Open Data Report (DGM Lab, 2021)

Technology application capability The overall index of integrated government service capability in provincial government Provincial Governments and Key Cities Online Government Service Capability Survey and Evaluation Report (E-Government Research Center, 2021)

Information infrastructure Information infrastructure index White Paper on the Evaluation of Regional Development Levels of Big Data in China (CCID, 2021)

Institutional construction Institutional construction index

Data resources Data resources index

Intellectual guarantee Intellectual guarantee index

Policy regulations Policy environment index

Economic strength GDP per capita China Statistics Annual (NBS, 2021)

Financial input General public expenses budget per capita

User demand General index of Big Data development in provincial capitals Big Data Blue Paper: China Big Data Development Report No.5 (Big Data Key Strategy Lab, 2020)

Inter-government competition Maximum in open data index of neighboring provinces China Local Government Open Data Report (DGM Lab, 2020)

Variables	Measurement indicators	Data sources
OGD performance	China Open Data Index	China Local Government Open Data Report (DGM Lab, 2021)
Technology application capability	The overall index of integrated government service capability in provincial government	Provincial Governments and Key Cities Online Government Service Capability Survey and Evaluation Report (E-Government Research Center, 2021)
Information infrastructure	Information infrastructure index	White Paper on the Evaluation of Regional Development Levels of Big Data in China (CCID, 2021)
Institutional construction	Institutional construction index
Data resources	Data resources index
Intellectual guarantee	Intellectual guarantee index
Policy regulations	Policy environment index
Economic strength	GDP per capita	China Statistics Annual (NBS, 2021)
Financial input	General public expenses budget per capita
User demand	General index of Big Data development in provincial capitals	Big Data Blue Paper: China Big Data Development Report No.5 (Big Data Key Strategy Lab, 2020)
Inter-government competition	Maximum in open data index of neighboring provinces	China Local Government Open Data Report (DGM Lab, 2020)

According to measurement indicators of the variables and the data sources, this research gets 31 provincial government raw data of variables of OGD performance, as show in Table 4. These data provide a comprehensive insight into the level of OGD performance and the contributing factors to it. We note that the data do not exhibit a normal skewed distribution, signifying its appropriateness for exploring the configuration paths by using fsQCA.

Table 4.

Raw data of variable.

Province	OGD performance	Information infrastructure	Technology application capability	Institutional construction	Data resources	Intellectual guarantee	Economic strength	Financial input	User demand	Policy regulations	Inter-government competition
Zhejiang	76.69	38.23	95.38	53.54	72.62	37.04	100738	12390	66	28.1	68.08
Shanghai	70.74	34.76	95.38	21.85	59.29	46.87	156803	29021	65.08	23.08	78.56
Shandong	66.1	31.49	83.59	33.62	66.39	26.36	71825	6514	47.66	39.45	23.11
Guizhou	51.13	26.81	92.02	43.14	54.31	11.47	46355	4932	43.7	68.82	42.58
Guangdong	49.26	48.33	95.38	48.61	55.22	61.51	88521	11216	68.8	46.54	32.44
Sichuan	44.25	30.85	90.18	48.61	47.6	30.77	58009	5084	42.95	27.3	50.96
Guangxi	43.39	27.52	84.41	33.62	38.43	13.24	44237	3462	26.84	41.32	50.96
Beijing	41.03	46.16	93.06	21.85	45.28	62.5	164158	25459	79.97	16.5	43.95
Tianjin	40.26	28.12	81.99	21.85	45.16	15.46	101068	12312	40.29	22.19	44.13
Fujian	38.9	31.72	89.09	29.99	48.22	24.38	105818	7750	45.22	48.16	78.56
Jiangxi	31.56	27.96	86.28	40.16	34.68	15.04	57065	5374	33.23	30.5	78.56
Chongqing	27.34	28.52	86.28	37	0	17.22	78173	6706	38.1	21.29	50.96
Ningxia	23.74	22.37	85.33	17.21	6.23	5.7	55021	6035	27.57	15.49	9.48
Hainan	19.06	23.42	84.23	21.85	32.11	7.24	55131	8636	26.48	16.5	45.33
Henan	18.33	31.19	87.38	53.54	28.6	23.77	54691	4310	39.72	38.16	59.77
Anhui	12.51	26.17	91.02	37	8.19	20.4	62411	5052	37.19	36.84	78.56
Jiangsu	11.44	40.21	93.06	43.14	24.34	38.75	121333	11226	62.63	27.3	78.56
Hebei	10.48	32.36	86.89	12.11	1.65	20.45	48302	5040	28.28	31.19	59.77
Gansu	9.48	23.54	76.15	26.09	0	9.53	35848	3304	18.64	26.48	42.58
Hubei	6.91	28.61	88.04	45.95	26.67	28.09	73687	4237	42.11	25.65	27.79
Shaanxi	6.66	25.63	78.08	45.95	0	23.17	66291.58	5824	34.67	16.5	42.58
Hunan	5.67	28.15	84.84	43.14	0.41	18.19	62900.25	4349	33.27	19.43	50.96
Shanxi	5.53	26.96	81.15	33.62	0	11.97	51051	6158	27.82	46.54	23.11
Qinghai	4.8	20.38	76.36	12.11	10.45	6.04	50845	4901	7.73	7.75	42.58
Heilongjiang	3.95	23.84	80.92	29.99	4.29	14.11	42432	3073	16.05	5.3	3.14
Jilin	3.14	22.74	83.9	21.85	0	14.26	50561	4032	19.89	14.45	3.95
Inner Mongolia	3.08	22.98	83.13	33.62	0	9.61	71640	8076	24.4	29.67	23.74
Xinjiang	2.13	21.75	73.15	12.11	0.41	8.37	53606	5855	18.84	8.93	9.48
Tibet	1.91	21.15	78.13	21.85	1.62	6.39	52280	6296	2.63	4.02	42.58
Liaoning	1.4	27.09	83.59	37	0	22.42	58629	6102	30.98	23.13	10.48
Yunnan	1.2	23.5	85.1	17.21	0	13.48	52047	4357	19.19	16.5	50.96

Notes: Since the Inter-governmental competitors of Ningxia, Heilongjiang, Jilin, Inner Mongolia, Xinjiang, and Liaoning were only included in the evaluation of the China Open Data Index starting from 2021, their inter-foreign competition data are based on the year 2021.

4.4. Data calibration

Data calibration is the crucial step of the fsQCA process. The purpose is to convert raw data into fuzzy sets, thus allowing variable matching fittings with external standards. The fsQCA employs the function calibrate (x, n1, n2, n3) to convert different types of data to [0.1], where x is the variable, and n1, n2, and n3 represent the threshold for calibration anchor points. In this research, the thresholds for full-set membership, intermediate-set membership, and full-set non-membership are set at 0.95, 0.5, and 0.05, respectively (Andrews et al., 2016; Zhao & Fan, 2021). The calibration values for the sample data, corresponding to the aforementioned thresholds, are listed in Table 5.

Table 5.
Variable calibration anchor.

Variable Anchor

Full membership Intersection No membership

OGD performance 68.42 12.51 1.66

Information infrastructure 43.19 27.52 21.45

Technology application capability 95.38 85.10 76.26

Institutional construction 51.08 33.62 12.11

Data resources 62.84 10.45 0

Intellectual guarantee 54.19 17.22 6.215

Economic strength 139068 58009 43334.50

Financial input 18924.50 5855 3383

User demand 67.40 33.27 11.89

Policy regulations 47.35 25.65 6.53

Inter-government competition 78.56 43.95 6.72

Variable	Anchor
OGD performance	68.42	12.51	1.66
Information infrastructure	43.19	27.52	21.45
Technology application capability	95.38	85.10	76.26
Institutional construction	51.08	33.62	12.11
Data resources	62.84	10.45	0
Intellectual guarantee	54.19	17.22	6.215
Economic strength	139068	58009	43334.50
Financial input	18924.50	5855	3383
User demand	67.40	33.27	11.89
Policy regulations	47.35	25.65	6.53
Inter-government competition	78.56	43.95	6.72

Omitting cases with a membership score of 0.5 poses a challenge when examining conditions (Ragin, 2008). To improve the consistency between samples and address this issue, this research followed the recommendation of Fiss (2011) by adding a constant of 0.001 to antecedent conditions with membership score below 1. The final membership scores as shown in Table 6.

Table 6.

Fuzzy set affiliation.

Province	OGD performance	Information infrastructure	Technology application capability	Institutional construction	Data resources	Intellectual guarantee	Economic strength	Financial input	User demand	Policy regulations	Inter-government competition
Zhejiang	0.971	0.891	0.951	0.971	0.971	0.831	0.831	0.821	0.951	0.581	0.891
Shanghai	0.961	0.801	0.951	0.161	0.941	0.921	0.971	1	0.941	0.401	0.951
Shandong	0.951	0.681	0.371	0.501	0.961	0.681	0.631	0.541	0.781	0.871	0.161
Guizhou	0.891	0.411	0.881	0.841	0.921	0.171	0.081	0.251	0.711	1	0.471
Guangdong	0.881	0.981	0.951	0.931	0.931	0.971	0.761	0.771	0.961	0.951	0.281
Sichuan	0.851	0.651	0.811	0.931	0.891	0.751	0.501	0.281	0.701	0.561	0.651
Guangxi	0.841	0.501	0.441	0.501	0.831	0.251	0.061	0.051	0.291	0.901	0.651
Beijing	0.821	0.971	0.911	0.161	0.881	0.981	0.981	0.991	0.981	0.191	0.501
Tianjin	0.821	0.531	0.261	0.161	0.881	0.381	0.831	0.811	0.651	0.371	0.501
Fujian	0.801	0.691	0.761	0.381	0.901	0.641	0.851	0.611	0.741	0.961	0.951
Jiangxi	0.741	0.521	0.591	0.751	0.801	0.361	0.451	0.361	0.501	0.661	0.951
Chongqing	0.691	0.551	0.591	0.641	0.051	0.501	0.681	0.551	0.601	0.341	0.651
Ningxia	0.651	0.071	0.521	0.091	0.231	0.041	0.351	0.511	0.311	0.171	0.061
Hainan	0.591	0.121	0.431	0.161	0.781	0.061	0.361	0.651	0.281	0.191	0.531
Henan	0.581	0.671	0.661	0.971	0.741	0.631	0.341	0.131	0.641	0.851	0.801
Anhui	0.501	0.341	0.851	0.641	0.341	0.561	0.541	0.271	0.591	0.821	0.951
Jiangsu	0.431	0.921	0.911	0.841	0.691	0.851	0.911	0.771	0.931	0.561	0.951
Hebei	0.361	0.721	0.631	0.051	0.071	0.571	0.121	0.271	0.331	0.681	0.801
Gansu	0.301	0.121	0.051	0.261	0.051	0.111	0.011	0.041	0.111	0.531	0.471
Hubei	0.181	0.551	0.701	0.891	0.721	0.711	0.641	0.121	0.691	0.501	0.211
Shaanxi	0.171	0.281	0.081	0.891	0.051	0.621	0.581	0.491	0.531	0.191	0.471
Hunan	0.131	0.531	0.481	0.841	0.051	0.521	0.551	0.141	0.501	0.271	0.651
Shanxi	0.131	0.431	0.211	0.501	0.051	0.191	0.191	0.521	0.321	0.951	0.161
Qinghai	0.111	0.031	0.051	0.051	0.501	0.051	0.191	0.241	0.031	0.061	0.471
Heilongjiang	0.091	0.141	0.201	0.381	0.151	0.301	0.041	0.031	0.081	0.041	0.041
Jilin	0.071	0.091	0.401	0.161	0.051	0.311	0.181	0.101	0.131	0.151	0.041
Inner Mongolia	0.071	0.101	0.341	0.501	0.051	0.111	0.621	0.621	0.221	0.641	0.161
Xinjiang	0.051	0.051	0.021	0.051	0.051	0.081	0.291	0.501	0.121	0.071	0.061
Tibet	0.051	0.041	0.091	0.161	0.071	0.051	0.241	0.531	0.011	0.031	0.471
Liaoning	0.041	0.451	0.371	0.641	0.051	0.601	0.511	0.511	0.421	0.401	0.061
Yunnan	0.041	0.121	0.501	0.091	0.051	0.271	0.231	0.141	0.121	0.191	0.651

5. Data analysis and results

5.1. Necessary condition analysis

Before configuration analysis is conducted, analyzing the degree of explanation of a single influencing variable on the outcome variable is necessary. The conditional variable with consistency over 0.9 will be considered a necessary condition that can explain the outcome variable independently. If the value of consistency is below 0.9, it indicates that this variable needs to act together with others to explain the outcome variable. Variables with consistency over 0.8 are regarded as sufficient conditions. Table 7 shows that technology application capability, data resources, and user demand have consistencies bigger than 0.8, indicating that they are sufficient conditions to improve OGD performance. All the influencing variables have consistencies lower than 0.9, meaning none of them can explain OGD performance alone. Configuration analysis on the influencing variables is needed to investigate the combinational influence of organizational, technological, and environmental factors on OGD performance.

Table 7.
Consistency and coverage analysis.

Variable Consistency Coverage

Information infrastructure 0.74 0.79

Technology application capability 0.82 0.76

Institutional construction 0.67 0.66

Data resources 0.86 0.86

Intellectual guarantee 0.70 0.74

Economic strength 0.72 0.73

Financial input 0.68 0.74

User demand 0.82 0.80

Policy regulations 0.74 0.73

Inter-government competition 0.76 0.71

Variable	Consistency	Coverage
Information infrastructure	0.74	0.79
Technology application capability	0.82	0.76
Institutional construction	0.67	0.66
Data resources	0.86	0.86
Intellectual guarantee	0.70	0.74
Economic strength	0.72	0.73
Financial input	0.68	0.74
User demand	0.82	0.80
Policy regulations	0.74	0.73
Inter-government competition	0.76	0.71

5.2. Configuration analysis

FsQCA contains three types of conditional variable configuration paths are derived –complex, parsimonious, and intermediate solution. Out of consideration for the solutions’ simplicity and range of coverage, the research chooses the intermediate solution of conditional variable combinations. Table 8 indicates that seven configuration paths are present for influencing variables to affect OGD performance. All paths have consistencies higher than 0.95, demonstrating these configuration paths possess stronger explanatory power. The total consistency is 0.96, revealing that for regions that can match these seven influencing combinations, about 96% of the provinces achieve higher OGD performance. The total coverage is 0.59, showing that these seven configuration paths of conditions can cover 59% of provinces with higher OGD performance.

Table 8.
Configuration paths that lead to high OGD performance.

Path 1 demonstrates that local governments meeting essential prerequisites, including information infrastructure, technology application capability, institutional construction, data resources, intellectual security, economic strength, user demand, policies and regulations, as well as inter-governmental competition, are poised to attain an outstanding level of OGD performance. This path can explain about 33% of cases. Apart from intellectual guarantee being a supplementary condition, all others are key conditions of this path. We term this path as “Technology-Organization-Environment combined driven”. Zhejiang Province has consistently prioritized information infrastructure construction, technological innovation, institutional development, data resource system construction, talent cultivation, and the establishment of relevant policies and regulations. Furthermore, powerful economic and financial strengths are the province’s hallmarks. Its provincial OGD platform going live earlier in due course catalyzed immense user demands. Geographically bordering Shanghai also brings competitive pressure to Zhejiang Province. Under combined actions of these conditions, Zhejiang Province comes top in the China Open Data Index. It is chosen as the quintessential representative of this path.

Path 2 indicates that local governments equipped with robust information infrastructure and a high level of technological application proficiency are confronted with inter-governmental competition from neighboring regions and increasing demands for OGD from the public and society. To achieve exemplary OGD performance, these local governments must develop and refine policies that demonstrate their strong economic capabilities, significant financial resources, and competitive advantages in data and human capital. This path can explain about 28% of cases. Information infrastructure, data resources, economic strength, financial input, and user demands are the key conditions, with technology application capability and intellectual guarantee the supplementary. We name this path “Economy-Talent-Demand driven”. As the representative cases of this path, the economies of Beijing and Shanghai are powerfully developing, their financial inputs are bountiful, and many universities supply great numbers of big data talents for OGD. With both regions practicing OGD the earliest, their public is also prompted to maintain very high demands and degree of participation in OGD, laying down a firm foundation for successful implementation of OGD.

Path 3 illuminates the potential for local governments to achieve a commendable level of OGD performance, even when facing substantial public demands and competitive pressures. This can be realized despite the absence of a comprehensive information infrastructure, substantial economic resources, sufficient financial investments, and human resource assurances. It becomes achievable when these governments simultaneously prioritize crucial factors such as advanced technological application capabilities, abundant data resources, the establishment of OGD institutions, and the enhancement of policy and regulatory frameworks. This path can explain about 21% of cases. For them, institutional construction, data resources, user demands, policy, and regulations are the key conditions, with technology application capability as the supplementary. We identify this path as “Institution-Data-Policies driven”. The economy of Guizhou Province is not so developed. However, through establishing a big data management organization, gathering and managing data resources stridently, and formulating multiple norms of OGD policies and standards, Guizhou Province achieves excellent OGD performance. Hence, Guizhou Province is chosen as the representative case of this path.

Path 4 illustrates that in regions endowed with robust information infrastructure, significant economic prowess, and ample financial resources, confronting substantial demands from the public and society necessitates a strategic approach. This involves initiating efforts by enhancing organizational structures and institutions, establishing comprehensive policies and regulations, building resilient data resource systems, and effectively showcasing human resource. Such actions are instrumental in propelling the advancement of OGD. This path can explain about 22% of cases. Information infrastructure, data resources, economic strength, financial input, and user demands are the key conditions, with institutional construction, intellectual guarantee, and policy regulations as the supplementary ones. We christen this path as “Institution-Economy-Demand driven”. Guangdong Province is the typical representative of this path. The province’s GDP and general public expenses budget are high on top nationally. It is also the first to set up a big data management organization. High-speed developments of the digital economy and in-depth usage of digital government also brought forth gigantic user demands, smoothly advancing the implementation of OGD together.

Path 5 reveals that in regions characterized by relatively limited economic strength and financial resources, compounded by fierce inter-government competition and high user demands, enhancing OGD performance hinges on more targeted efforts. Apart from possessing foundational organizational prerequisites, these regions must prioritize the development of information infrastructure, bolster technical management and application capabilities, leverage available data resources, and refine the construction of policies and regulatory frameworks. This path can explain about 31% of cases. Information infrastructure, institutional construction, data resources, policy regulations, and inter-government competition are the key conditions, while technology application capability and user demand as the supplementary ones. We denote this path as “Organization-Policies-Competition driven”. Being an archetype of this path, Jiangxi Province facing competitive pressure and demonstration drive from Zhejiang and Guangdong has achieved a high level of OGD performance through the establishment of institutions, policies, and other measures to promote OGD applications.

Path 6 unveils a strategy for regions characterized by relatively limited technology application capabilities and intellectual safeguards, coupled with deficiencies in institutional development and policy regulation, yet facing considerable inter-governmental competition. In such contexts, these regions should heed user demands and leverage their substantial economic strength and sufficient financial allocations. By prioritizing the enhancement of information infrastructure and data resource systems, they can propel themselves toward the attainment of high-level of OGD performance. This path can explain about 16% of cases. It involves information infrastructure, data resources, economic strength, financial input, user demands, and inter-governmental competition as key conditions. We call this path “Data-Economy-Demand driven”. Tianjin with a high economic standard fully made use of advantages in information infrastructure, financial input, and data resources to achieve high standard OGD performance. It is a typical representative of this path.

Path 7 underscores the imperative of proactive measures for economically underdeveloped regions, characterized by lower technology application capabilities, a shortage of big data expertise, limited financial resources, and less pronounced user demands, yet faced with substantial competitive pressures from neighboring regions. In such circumstances, the primary focus should be on the formulation and execution of regulations and policies pertaining to OGD. Additionally, they should support these efforts by bolstering the construction of information infrastructure, enhancing organizational and institutional frameworks, and establishing comprehensive data resource systems. This path can explain about 23% of cases. Information infrastructure, institutional construction, data resources, policy regulations, and inter-government competition are the key conditions generating this path. The path is considered “Data-Policies-Competition driven”. Guangxi is challenged by competitive pressures from Guizhou and Guangdong. Through strengthening the systems in data resources and policy regulations, Guangxi achieved high-level OGD performance and thus is selected as the typical example of this path.

5.3. Potential substitution relationships between the conditions

Through the analysis of various configuration paths, this paper uncovers substitution relationships between the condition variables that significantly influence OGD performance. For example, by comparing path 2 and path 4, this paper identifies a correlation between institutional construction and inter-government competition. Local governments that have not yet established OGD institutions but are experiencing significant competitive pressures from neighboring regions can draw from the successful experiences of advanced local governments to leverage their existing technical, data, economic, and financial resources to meet user needs and improve their OGD performance. On the contrary, local governments without high levels of competitive pressure can still achieve excellent OGD performance by establishing a specialized agency and assigning dedicated staff responsible for implementing OGD. By formulating OGD policies, they can reduce resistance to coordination among different government agencies in implementing OGD (Zhao & Fan, 2021).

Through comparative analysis of path 3 and path 7, this paper highlights the observable interdependence of information infrastructure and technology application capability, as well as the relationship between user demand and inter-governmental competition. If an organization possesses technology application capability and user demand, but lacks sufficient information infrastructure or faces minimal inter-governmental competition, the implementation of specialized agency support and well-crafted OGD policies may result in performance improvement. However, in the event of competitive external pressures, but with a lack of technology application capability or user demand, it is recommended that the government prioritizes the strengthening of information infrastructure, adjusts organizational arrangements, and fully maximizes data resources through the development of OGD policies to enhance performance.

5.4. Robustness test

According to Schneider and Wagemann’s (2012) research, the initial consistency threshold chosen by researchers has an impact on the number of truth table rows and, as a result, affects the final analysis outcomes. This study utilizes the adjusted consistency level method proposed by Ordanini et al. (2014) and White et al. (2021). Precisely, we raise the consistency threshold to 0.85 while maintaining a case frequency threshold of 1 and an unchanged PRI of 0.75. The configurations of antecedent conditions obtained were compared with the outcomes in Table 6. Results illustrate that the combined paths and their respective core and edge conditions are largely unaltered, providing further proof of the solidity of our study’s findings.

6. Discussion

This paper employs the fsQCA to explore the ways to improve the OGD performance of local governments. The configuration analysis identifies seven configuration paths which can show the combined effect of technical conditions, organizational resources, external environment, as well as the characteristics of multi-path dependence in OGD performance. This finding is identical with the conclusions of previous research (Janssen et al., 2012; Hossain et al., 2021; Zhao & Fan, 2021; Zhao et al., 2022). OGD performance depends on the combination of ten conditions: information infrastructure, technology application capability, institutional construction, data resources, intellectual guarantee, economic strength, financial input, policy regulations, user demand, and Inter-government competition, and a single condition cannot promote the development of OGD.

With respect to the technical conditions, the establishment of high-quality information infrastructure forms the bedrock of technological development and creates a favorable environment for promoting OGD. Within the seven paths examined in this study, information infrastructure serves as the fundamental precondition for paths 1, 2, 4, 5, 6, and 7, and has a substitutive relationship with technological application capability between paths 3 and 7. This presents information infrastructure as a significant influencing factor in local governments’ OGD performance. The advent of the Internet and various information and communication technologies (ICTs) have reduced the cost of acquiring, managing, and using information, thereby creating an avenue for stakeholders in the OGD ecosystem to interact and communicate effectively (Shin, 2015; Styrin, 2017). ICTs have broad-ranging applications throughout society. Mhlanga (2006) assert that it is imperative for governments, businesses, and other stakeholders to work together to maximize the societal and economic benefits that ICTs offer, all while actively addressing and mitigating the risks of exclusion. Through optimal utilization of organizational resources and external factors, local governments can leverage information infrastructure to establish efficient social and technical channels to share and transmit data, information, and innovative ideas between government and non-government entities and individuals. Information infrastructure serves as the technical supporting force for the OGD performance of local governments. Harrison, Pardo, and Meghan (2012) identification of the role of ICT might be thought as the social infrastructure of OGD ecosystem is consistent with this finding.

Regarding the organizational resources, organizational possession and open access to high-quality, valuable government data resources are essential in enhancing OGD performance. In this research, data resources serve as the key condition across all 7 paths, which indicates that data resources are the most critical condition of local governments’ OGD performance. Open government data is an important public data asset and crucial production factor for the big data industry (Congress U.S., 2018). The fluidity and accessibility of government data provide an essential foundation for the development of the big data industry (European Commission, 2011). OGD organizations need to develop metadata standards and utilize technology to integrate data resources, and then publish them in machine-readable formats on OGD platform (Dawes et al., 2016; Zhao & Fan, 2018). Hoffmann (2012) demonstrated that users can convert extensive open data into valuable information and knowledge through analysis, and then make quick decisions based on this knowledge. This is an important manifestation of achieving OGD performance, and data resources are likely to be served as the organizational thrust for local governments’ OGD performance.

Inter-governmental competition is an important external environmental condition that influences governments’ performance (Berry & Berry, 1999; Bearfield & Bowman, 2017; Ma, 2014). In the external environmental conditions of OGD performance, inter-governmental competition is the key condition in paths 1, 2, 5, 6, and 7, which reflects its contribution to OGD performance. Inter-governmental competition is an important mechanism influencing local governments’ OGD performance (Zhao & Fan, 2021; Zhao et al., 2022). When local governments have better technological and organizational conditions, the rational use of intense inter-government competition can help to enhance the level of OGD performance. Zhao and Fan (2021) suggested that in the face of competition from neighboring provinces, government can improve OGD performance by adjusting strategies between technical conditions and organizational resources. At this level, the Inter-governmental competition provides the environmental pulling force for local governments’ OGD performance.

7. Conclusions

OGD plays a significant role in promoting open government, digital economy, and sustainable development. However, owing to differences in consciousness, resources and capability, OGD performance varies across regions. Based on TOE framework and the OGD practice of 31 Chinese provincial governments, we construct a research model and employ fsQCA to explore the configuration paths of OGD performance. The analysis of these cases confirmed that individual condition cannot be deemed as necessary conditions of OGD performance. Adequate technology application capability, ample data resources, and substantial user demands represent essential prerequisites for enhancing OGD performance. Furthermore, factors such as robust information infrastructure, well-developed institutional frameworks, intellectual safeguards, financial investments, compliance with mandatory policies and regulations, as well as competitive pressures, play pivotal roles in the execution of OGD. Results demonstrate seven configuration paths could be utilized to achieve high-level OGD performance. By analyzing the potential substitution relationships between different conditions, this paper also finds that information infrastructure serve as the technical supporting force for OGD performance, the data resources is the organizational thrust for OGD performance, and the Inter-governmental competition provides the environmental pulling force for OGD performance.

8. Research implications

This study is part of a larger effort to obtain the configuration paths of OGD performance, especially local governments. The theoretical implications are mainly as follows: (1) Based on the TOE framework, this study constructs a research model by considering 10 conditions of OGD performance. This contributes to the theoretical literature by providing a multidimensional and configuration-based perspective for improving OGD performance. (2) This study delves into the intricate conditional factors influencing the OGD performance of local governments. It indicates that achieving high-level OGD performance is contingent upon the interplay and alignment of technological, organizational, and environmental conditions. This perspective underscores the dynamic interdependence among factors encompassing technology, organizational structure, human resources, and material resources pertaining to OGD within a specific context. (3) This study highlights the effectiveness of TOE framework and fsQCA in identifying the key factors and conditions of OGD performance. The synergy between the TOE framework and fsQCA offers a valuable heuristic approach for exploring the configuration paths that contribute to achieving high-level OGD performance.

With respect to the management contributions, this research demonstrates: (1) OGD performance depends on the combination of technical, organizational, and environmental conditions. A comprehensive approach encompassing organization, technology, and environmental dimensions can be adopted to improve OGD performance. (2) Information infrastructure is crucial for achieving high-level OGD performance, particularly with emerging technologies. The government should pay further attention to strengthening the construction of information infrastructure and the capability to make use of emerging digital technologies. (3) Local governments should focus on institutional development and allocate more resources to organizational conditions enhancement. This can be achieved by establishing a specialized OGD entity and recruiting highly skilled professionals in the field of big data to spearhead OGD efforts. Furthermore, it’s necessary to provide substantial economic support and adequate financial investment, as well as strengthen data governance throughout OGD’s lifecycle. (4) The external environment serves as fundamental prerequisites for OGD. Ongoing efforts to refine policies, regulations and standards are essential to offer guidance for OGD. It is also imperative to facilitate the comprehensive disclosure of government data in accordance with users’ requirements. Additionally, regular OGD evaluations and experience-sharing sessions should be conducted to improve OGD performance through healthy competition.

9. Limitations and future studies

There are some limitations in this research. Firstly, in order to maintain consistency, only one year of data was collected from 31 provincial governments. This limited dataset may impact the generalizability of our findings. Future studies should aim to obtain a more comprehensive dataset that encompasses a wider range of levels, regions, years, and even international contexts. Additionally, while this research has provided insights into the mechanisms through which various factors influence OGD performance, the precise magnitude of influence attributed to each factor remains unknown. Therefore, further studies may use quantitative research methods to analyze the extent to which individual factors exert influence on OGD performance.

Footnotes

The supplementary material is available in the electronic version of this article: .

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Variable	Anchor
	Full membership	Intersection	No membership
OGD performance	68.42	12.51	1.66
Information infrastructure	43.19	27.52	21.45
Technology application capability	95.38	85.10	76.26
Institutional construction	51.08	33.62	12.11
Data resources	62.84	10.45	0
Intellectual guarantee	54.19	17.22	6.215
Economic strength	139068	58009	43334.50
Financial input	18924.50	5855	3383
User demand	67.40	33.27	11.89
Policy regulations	47.35	25.65	6.53
Inter-government competition	78.56	43.95	6.72

Improving Open Government Data Performance of Local Governments: A Configurational Analysis Based on TOE Framework

Abstract

Keywords

1. Introduction

2. Literature review

2.1. OGD development and its performance evaluation

2.2. The influencing factors of OGD performance

3. Analytical framework

3.2. Organizational resources

3.3. External environment

4. Methods and data

4.1. Research methodology

4.2. Research sample

4.3. Data collection

5.1. Necessary condition analysis

Table 8. Configuration paths that lead to high OGD performance.

5.4. Robustness test

6. Discussion

7. Conclusions

8. Research implications

9. Limitations and future studies

Footnotes

References

Table 8.
Configuration paths that lead to high OGD performance.